





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case								=							{
    """configuration_albert""": ["""ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """AlbertConfig""", """AlbertOnnxConfig"""],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      snake_case								=							["""AlbertTokenizer"""]

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      snake_case								=							["""AlbertTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      snake_case								=							[
          """ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """AlbertForMaskedLM""",
          """AlbertForMultipleChoice""",
          """AlbertForPreTraining""",
          """AlbertForQuestionAnswering""",
          """AlbertForSequenceClassification""",
          """AlbertForTokenClassification""",
          """AlbertModel""",
          """AlbertPreTrainedModel""",
          """load_tf_weights_in_albert""",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      snake_case								=							[
          """TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """TFAlbertForMaskedLM""",
          """TFAlbertForMultipleChoice""",
          """TFAlbertForPreTraining""",
          """TFAlbertForQuestionAnswering""",
          """TFAlbertForSequenceClassification""",
          """TFAlbertForTokenClassification""",
          """TFAlbertMainLayer""",
          """TFAlbertModel""",
          """TFAlbertPreTrainedModel""",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      snake_case								=							[
          """FlaxAlbertForMaskedLM""",
          """FlaxAlbertForMultipleChoice""",
          """FlaxAlbertForPreTraining""",
          """FlaxAlbertForQuestionAnswering""",
          """FlaxAlbertForSequenceClassification""",
          """FlaxAlbertForTokenClassification""",
          """FlaxAlbertModel""",
          """FlaxAlbertPreTrainedModel""",
      ]

if TYPE_CHECKING:
      from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig, AlbertOnnxConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_albert import AlbertTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_albert_fast import AlbertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_albert import (
                ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                AlbertForMaskedLM,
                AlbertForMultipleChoice,
                AlbertForPreTraining,
                AlbertForQuestionAnswering,
                AlbertForSequenceClassification,
                AlbertForTokenClassification,
                AlbertModel,
                AlbertPreTrainedModel,
                load_tf_weights_in_albert,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_albert import (
                TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFAlbertForMaskedLM,
                TFAlbertForMultipleChoice,
                TFAlbertForPreTraining,
                TFAlbertForQuestionAnswering,
                TFAlbertForSequenceClassification,
                TFAlbertForTokenClassification,
                TFAlbertMainLayer,
                TFAlbertModel,
                TFAlbertPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_albert import (
                FlaxAlbertForMaskedLM,
                FlaxAlbertForMultipleChoice,
                FlaxAlbertForPreTraining,
                FlaxAlbertForQuestionAnswering,
                FlaxAlbertForSequenceClassification,
                FlaxAlbertForTokenClassification,
                FlaxAlbertModel,
                FlaxAlbertPreTrainedModel,
            )
else:
      import sys

      snake_case								=							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




'''simple docstring'''



from collections import defaultdict
def        UpperCAmelCase (    A	:					int					):
							SCREAMING_SNAKE_CASE       :   List[Any]         =      1
							SCREAMING_SNAKE_CASE       :   Dict         =      True
							for v in tree[start]:
														if v not in visited:
																					ret += dfs(A					)
							if ret % 2 == 0:
														cuts.append(A					)
							return ret
def        UpperCAmelCase (    ):
							dfs(1					)


if __name__ == "__main__":
							lowerCAmelCase_      ,				lowerCAmelCase_  :							Dict             =    10, 9
							lowerCAmelCase_  :							Dict             =    defaultdict(list)
							lowerCAmelCase_  :							dict[int, bool]             =    {}
							lowerCAmelCase_  :							list[int]             =    []
							lowerCAmelCase_  :							Optional[Any]             =    0
							lowerCAmelCase_  :							Dict             =    [(2, 1), (3, 1), (4, 3), (5, 2), (6, 1), (7, 2), (8, 6), (9, 8), (10, 8)]
							for u, v in edges:
														tree[u].append(v)
														tree[v].append(u)
							even_tree()
							print(len(cuts) - 1)




